Face personalization aims to insert specific faces, taken from images, into
pretrained text-to-image diffusion models. However, it is still challenging for
previous methods to preserve both the identity similarity and editability due
to overfitting to training samples. In this paper, we propose Face2Diffusion
(F2D) for high-editability face personalization. The core idea behind F2D is
that removing identity-irrelevant information from the training pipeline
prevents the overfitting problem and improves editability of encoded faces. F2D
consists of the following three novel components: 1) Multi-scale identity
encoder provides well-disentangled identity features while keeping the benefits
of multi-scale information, which improves the diversity of camera poses. 2)
Expression guidance disentangles face expressions from identities and improves
the controllability of face expressions. 3) Class-guided denoising
regularization encourages models to learn how faces should be denoised, which
boosts the text-alignment of backgrounds. Extensive experiments on the
FaceForensics++ dataset and diverse prompts demonstrate our method greatly
improves the trade-off between the identity- and text-fidelity compared to
previous state-of-the-art methods.

提出 Face2Diffusion (F2D) 用于高可编辑性的人脸个性化，核心思想是从训练管线中去除与身份无关的信息，改善了编码人脸的可编辑性。

面孔扩散以实现快速和可编辑的面部个性化

Face2Diffusion for Fast and Editable Face Personalization

Numerous attempts have been made to the task of person-agnostic face swapping
given its wide applications. While existing methods mostly rely on tedious
network and loss designs, they still struggle in the information balancing
between the source and target faces, and tend to produce visible artifacts. In
this work, we introduce a concise and effective framework named StyleSwap. Our
core idea is to leverage a style-based generator to empower high-fidelity and
robust face swapping, thus the generator's advantage can be adopted for
optimizing identity similarity. We identify that with only minimal
modifications, a StyleGAN2 architecture can successfully handle the desired
information from both source and target. Additionally, inspired by the ToRGB
layers, a Swapping-Driven Mask Branch is further devised to improve information
blending. Furthermore, the advantage of StyleGAN inversion can be adopted.
Particularly, a Swapping-Guided ID Inversion strategy is proposed to optimize
identity similarity. Extensive experiments validate that our framework
generates high-quality face swapping results that outperform state-of-the-art
methods both qualitatively and quantitatively.

本文提出了一种名为 StyleSwap 的简洁而有效的框架，将基于风格的生成器用于高保真和强大的人脸交换，可通过优化标识相似性来实现生成器的优势，并提出了 Swapping-Driven Mask Branch 和 Swapping-Guided ID Inversion 策略来进一步改进信息混合，实验证明本框架可产生高质量的人脸交换结果，优于现有最先进方法。